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EFFECT OF FOOT REFLEXOLOGY VERSUS ACUPRESSURE ON SLEEP DISTURBANCES AND HOT FLASHES IN POSTMENOPAUSAL WOMEN

2025· article· en· W4414917263 on OpenAlexaff
Yasmeen S Abdelazeem, Walid Mohamed Elnagar, Shreen R. Abdoelmagd, Mohamed A. Awad, Essam A. Hassan

Bibliographic record

VenueDeraya International Journal for Medical Sciences and Rehabilitation · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsCanadian Physiotherapy Association
Fundersnot available
KeywordsReflexologyPittsburgh Sleep Quality IndexFoot (prosody)AcupressureHot flashQuality of life (healthcare)Sleep quality

Abstract

fetched live from OpenAlex

Objective: The aim of the study was to find out the impact of foot reflexology versus acupressure on sleep disturbances and hot flashes in postmenopausal women. Methods: Fifty-four postmenopausal women suffering from postmenopausal sleep disturbances and hot flashes were randomly allocated into two equal groups: Group A, (n=27) treated by foot reflexology for fifteen minutes, 3 sessions per week for six weeks, and Group B, (n=27) treated by acupressure for twenty-one minutes, 3 sessions per week for six weeks. Sleep quality was evaluated by Pittsburgh Sleep Quality Index, and hot flashes severity by Hot Flashes Questionnaire before and after six weeks of the treatment protocol. Results: There were statistically significant improvements (p < 0.05) in pittsburgh sleep quality index score, and hot flashes questionnaire score in both groups after treatment compared with baseline. When comparing both groups, post-treatment results revealed significant improvements in pittsburgh sleep quality index score, and hot flashes questionnaire score (p < 0.001) in favor of group (B). Conclusion: Both acupressure, as well as foot reflexology were effective therapeutic modality for management of postmenopausal women with superior to acupressure for gained improvements in sleep quality, and severity of hot flashes among postmenopausal women.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.501
Teacher spread0.476 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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